abstract = "We propose a new type of leaf node for use in Symbolic
Regression (SR) that performs linear combinations of
feature variables (LCF). LCF's weights are tuned using
a gradient method based on back-propagation algorithm
known from neural networks. Multi-Gene Genetic
Programming (MGGP) was chosen as a baseline model. As a
sanity check, we experimentally show that LCFs improve
the performance of the baseline on a rotated toy SR
problem. We then perform a thorough experimental study
on a number of artificial and real-world SR benchmarks.
The usage of LCFs in MGGP statically improved the
results in 5 cases out of 9, while it worsen them in
only a single case.",

notes = "Also known as \cite{Zegklitz:2017:LCF:3067695.3076009}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",